6d8c18cd1a* Replace standalone Studio wording with Unsloth Replace the single word Studio with Unsloth wherever it is used as shorthand for Unsloth Studio in docs, CLI output, UI strings, i18n locales, workflow display names, comments and docstrings. Kept unchanged: the full name Unsloth Studio, third party product names (LM Studio, Visual Studio, Mac Studio), feature names (Recipe Studio, Fine-tuning Studio and its translations), and all identifiers such as env vars, commands, paths and filenames. * Address review feedback on the Studio wording rename Use "an" before Unsloth where the rename left the article as "a". Restore the split brand where Unsloth and Studio render as two halves of the full product name: the onboarding sidebar subtitle and the IPv6 localhost warning. Scope two messages to the full name Unsloth Studio where plain Unsloth was misleading: the AMD README bullet and the CLI studio setup error.
278 lines
12 KiB
Python
278 lines
12 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
|
|
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
|
|
|
|
"""Per-MTok pricing tables and ``calculate_cost`` (usage block -> USD).
|
|
|
|
Sources: Anthropic prompt-caching docs (5m write 1.25x, 1h write 2x,
|
|
read 0.1x), web search ($10/1000), code execution; OpenAI pricing page.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
from typing import Any, Optional
|
|
|
|
# Per-MTok base USD. Cache multipliers apply to `input_per_mtok`
|
|
# (not absolute prices), per Anthropic docs.
|
|
ANTHROPIC_PRICING: dict[str, dict[str, float]] = {
|
|
"claude-opus-4-7": {"input_per_mtok": 5.0, "output_per_mtok": 25.0},
|
|
"claude-opus-4-6": {"input_per_mtok": 5.0, "output_per_mtok": 25.0},
|
|
# Alias bare + dated id: backend defaults use the bare form, which
|
|
# won't prefix-match the dated key.
|
|
"claude-opus-4-5": {"input_per_mtok": 5.0, "output_per_mtok": 25.0},
|
|
"claude-opus-4-5-20251101": {"input_per_mtok": 5.0, "output_per_mtok": 25.0},
|
|
"claude-opus-4-1": {"input_per_mtok": 15.0, "output_per_mtok": 75.0},
|
|
"claude-opus-4-1-20250805": {"input_per_mtok": 15.0, "output_per_mtok": 75.0},
|
|
"claude-opus-4-20250514": {"input_per_mtok": 15.0, "output_per_mtok": 75.0},
|
|
"claude-sonnet-4-6": {"input_per_mtok": 3.0, "output_per_mtok": 15.0},
|
|
"claude-sonnet-4-5": {"input_per_mtok": 3.0, "output_per_mtok": 15.0},
|
|
"claude-sonnet-4-5-20250929": {"input_per_mtok": 3.0, "output_per_mtok": 15.0},
|
|
"claude-sonnet-4-20250514": {"input_per_mtok": 3.0, "output_per_mtok": 15.0},
|
|
"claude-haiku-4-5": {"input_per_mtok": 1.0, "output_per_mtok": 5.0},
|
|
"claude-haiku-4-5-20251001": {"input_per_mtok": 1.0, "output_per_mtok": 5.0},
|
|
}
|
|
|
|
OPENAI_PRICING: dict[str, dict[str, float]] = {
|
|
# Verified against developers.openai.com/api/docs/pricing.
|
|
# `long_context_*` keys apply past the threshold (gpt-5.5/5.4: 272k);
|
|
# families without them ship a single rate.
|
|
"gpt-5.5": {
|
|
"input_per_mtok": 5.0,
|
|
"output_per_mtok": 30.0,
|
|
"long_context_threshold": 272_000,
|
|
"long_context_input_per_mtok": 10.0,
|
|
"long_context_output_per_mtok": 45.0,
|
|
},
|
|
"gpt-5.5-pro": {"input_per_mtok": 30.0, "output_per_mtok": 180.0},
|
|
"gpt-5.4": {
|
|
"input_per_mtok": 2.5,
|
|
"output_per_mtok": 15.0,
|
|
"long_context_threshold": 272_000,
|
|
"long_context_input_per_mtok": 5.0,
|
|
"long_context_output_per_mtok": 22.5,
|
|
},
|
|
"gpt-5.4-pro": {"input_per_mtok": 30.0, "output_per_mtok": 180.0},
|
|
"gpt-5.4-mini": {"input_per_mtok": 0.75, "output_per_mtok": 4.5},
|
|
"gpt-5.4-nano": {"input_per_mtok": 0.20, "output_per_mtok": 1.25},
|
|
"gpt-5.3-codex": {"input_per_mtok": 1.75, "output_per_mtok": 14.0},
|
|
# chat-latest aliases gpt-5.5.
|
|
"gpt-5.3-chat-latest": {"input_per_mtok": 5.0, "output_per_mtok": 30.0},
|
|
"chat-latest": {"input_per_mtok": 5.0, "output_per_mtok": 30.0},
|
|
# o-series / gpt-4.5 left off the pricing page: omit so calculate_cost
|
|
# returns priced=False instead of silently $0.
|
|
}
|
|
|
|
# Shared multipliers (all Anthropic models).
|
|
ANTHROPIC_CACHE_5M_WRITE_MULT = 1.25
|
|
ANTHROPIC_CACHE_1H_WRITE_MULT = 2.0
|
|
ANTHROPIC_CACHE_READ_MULT = 0.1
|
|
# Anthropic fast-mode (Opus 4.6/4.7 only): 6x on input + output.
|
|
# https://platform.claude.com/docs/en/build-with-claude/fast-mode#pricing
|
|
ANTHROPIC_FAST_MODE_MULT = 6.0
|
|
|
|
# OpenAI: cache reads 0.1x; cache writes pay input price.
|
|
OPENAI_CACHE_READ_MULT = 0.1
|
|
|
|
# Server-tool surcharges. Anthropic code_exec: $0.05/hr marginal
|
|
# (50 free hours/day per org, not shown here).
|
|
ANTHROPIC_WEB_SEARCH_USD_PER_1K = 10.0
|
|
ANTHROPIC_CODE_EXEC_USD_PER_HOUR = 0.05
|
|
|
|
# OpenAI container bills per memory tier; report the 1g default
|
|
# ($0.09/hr) since the tier isn't surfaced to the ledger.
|
|
OPENAI_WEB_SEARCH_USD_PER_1K = 10.0
|
|
OPENAI_CONTAINER_USD_PER_HOUR = 0.09 # 1g default tier
|
|
|
|
|
|
def _lookup(provider: str, model: str) -> Optional[dict[str, float]]:
|
|
table = (
|
|
ANTHROPIC_PRICING
|
|
if provider == "anthropic"
|
|
else OPENAI_PRICING
|
|
if provider == "openai"
|
|
else None
|
|
)
|
|
if table is None:
|
|
return None
|
|
if model in table:
|
|
return table[model]
|
|
# Longest-prefix match on a dash boundary: dated snapshots inherit
|
|
# canonical prices, but "claude-opus-4-15" won't match "claude-opus-4-1".
|
|
for key in sorted(table, key = len, reverse = True):
|
|
if model.startswith(key) and (len(model) == len(key) or model[len(key)] == "-"):
|
|
return table[key]
|
|
return None
|
|
|
|
|
|
def calculate_cost(provider: str, model: str, usage: dict[str, Any]) -> dict[str, float]:
|
|
"""Return a per-turn USD cost breakdown (per-bucket + total).
|
|
|
|
Unknown model -> ``priced`` False and USD fields 0.0 (token counts still report).
|
|
"""
|
|
prices = _lookup(provider, model)
|
|
out: dict[str, float] = {
|
|
"input_usd": 0.0,
|
|
"output_usd": 0.0,
|
|
"cache_write_usd": 0.0,
|
|
"cache_read_usd": 0.0,
|
|
"server_tools_usd": 0.0,
|
|
"total_usd": 0.0,
|
|
"billable_input_tokens": 0,
|
|
"billable_output_tokens": 0,
|
|
"model_priced": model if prices else "",
|
|
"priced": bool(prices),
|
|
}
|
|
|
|
# Accept raw (input_tokens/output_tokens) and Unsloth chat-style
|
|
# (prompt_tokens/completion_tokens) envelopes. Cache buckets differ:
|
|
# raw Anthropic: input_tokens EXCLUDES cache buckets
|
|
# raw OpenAI: input_tokens INCLUDES cache_read
|
|
# Unsloth Anthropic: prompt_tokens INCLUDES cache_creation + cache_read
|
|
# Unsloth OpenAI: prompt_tokens == raw input_tokens
|
|
# Clamp >=0 so corrupted payloads can't produce a negative bill.
|
|
cache_creation = max(0, int(usage.get("cache_creation_input_tokens") or 0))
|
|
cache_read_native_present = (
|
|
"cache_read_input_tokens" in usage and usage.get("cache_read_input_tokens") is not None
|
|
)
|
|
cache_read = max(0, int(usage.get("cache_read_input_tokens") or 0))
|
|
# Fall back to mirrored prompt_tokens_details only when native
|
|
# cache_read_input_tokens is absent; an explicit native 0 is
|
|
# authoritative, so a stale proxy mirror can't inflate cache_read.
|
|
if not cache_read_native_present:
|
|
details = usage.get("prompt_tokens_details") or {}
|
|
if isinstance(details, dict):
|
|
cache_read = max(0, int(details.get("cached_tokens") or 0))
|
|
has_input_tokens = "input_tokens" in usage and usage.get("input_tokens") is not None
|
|
if has_input_tokens:
|
|
input_tokens = max(0, int(usage.get("input_tokens") or 0))
|
|
else:
|
|
# Chat-style: peel cache buckets back out for Anthropic to get
|
|
# the raw uncached prompt count.
|
|
prompt_tokens = max(0, int(usage.get("prompt_tokens") or 0))
|
|
if provider == "anthropic":
|
|
input_tokens = max(0, prompt_tokens - cache_creation - cache_read)
|
|
else:
|
|
input_tokens = prompt_tokens
|
|
# Prefer raw output_tokens even when 0 (an `or` would pick a stale
|
|
# completion_tokens).
|
|
if "output_tokens" in usage and usage.get("output_tokens") is not None:
|
|
output_tokens = max(0, int(usage.get("output_tokens") or 0))
|
|
else:
|
|
output_tokens = max(0, int(usage.get("completion_tokens") or 0))
|
|
if provider == "openai":
|
|
# Cached tokens land on input_tokens_details (raw Responses) or
|
|
# prompt_tokens_details (Unsloth chat-style).
|
|
for key in ("input_tokens_details", "prompt_tokens_details"):
|
|
details = usage.get(key) or {}
|
|
if isinstance(details, dict):
|
|
cache_read = max(cache_read, int(details.get("cached_tokens") or 0))
|
|
# OpenAI input_tokens already counts cache_read.
|
|
out["billable_input_tokens"] = input_tokens + cache_creation
|
|
else:
|
|
# Anthropic input_tokens excludes cache buckets; add them back.
|
|
out["billable_input_tokens"] = input_tokens + cache_creation + cache_read
|
|
out["billable_output_tokens"] = output_tokens
|
|
|
|
if not prices:
|
|
return out
|
|
|
|
# Long-context tier: whole-turn flip (not per-token blend) once
|
|
# billable_input_tokens crosses the threshold.
|
|
lc_thresh = prices.get("long_context_threshold")
|
|
in_long_context_tier = (
|
|
lc_thresh is not None
|
|
and out["billable_input_tokens"] >= int(lc_thresh)
|
|
and "long_context_input_per_mtok" in prices
|
|
and "long_context_output_per_mtok" in prices
|
|
)
|
|
if in_long_context_tier:
|
|
base = prices["long_context_input_per_mtok"]
|
|
out_per = prices["long_context_output_per_mtok"]
|
|
out["model_priced"] = f"{model} (long-context >{lc_thresh})"
|
|
else:
|
|
base = prices["input_per_mtok"]
|
|
out_per = prices["output_per_mtok"]
|
|
|
|
# Anthropic fast-mode: 6x on input + output. Cache multipliers stack
|
|
# on top, so applying once to (base, out_per) flows into the
|
|
# cache_*_usd buckets below.
|
|
if provider == "anthropic" and usage.get("speed") == "fast":
|
|
base *= ANTHROPIC_FAST_MODE_MULT
|
|
out_per *= ANTHROPIC_FAST_MODE_MULT
|
|
if out["model_priced"]:
|
|
out["model_priced"] = f"{out['model_priced']} (fast)"
|
|
|
|
out["input_usd"] = (input_tokens / 1_000_000.0) * base
|
|
out["output_usd"] = (output_tokens / 1_000_000.0) * out_per
|
|
|
|
if provider == "anthropic":
|
|
# Split cache_creation into 5m / 1h buckets when surfaced.
|
|
# Tolerate non-dict (some proxies fold to an int total).
|
|
cc_raw = usage.get("cache_creation")
|
|
cc_breakdown = cc_raw if isinstance(cc_raw, dict) else {}
|
|
cc_5m = max(0, int(cc_breakdown.get("ephemeral_5m_input_tokens") or 0))
|
|
cc_1h = max(0, int(cc_breakdown.get("ephemeral_1h_input_tokens") or 0))
|
|
if cc_5m + cc_1h == 0 and cache_creation > 0:
|
|
# No breakdown -- assume default 5m pool.
|
|
cc_5m = cache_creation
|
|
out["cache_write_usd"] = (cc_5m / 1_000_000.0) * base * ANTHROPIC_CACHE_5M_WRITE_MULT + (
|
|
cc_1h / 1_000_000.0
|
|
) * base * ANTHROPIC_CACHE_1H_WRITE_MULT
|
|
out["cache_read_usd"] = (cache_read / 1_000_000.0) * base * ANTHROPIC_CACHE_READ_MULT
|
|
# Server-tool surcharges.
|
|
srv = usage.get("server_tool_use") or {}
|
|
if isinstance(srv, dict):
|
|
web_searches = int(srv.get("web_search_requests") or 0)
|
|
code_exec_hours = float(srv.get("code_execution_hours") or 0.0)
|
|
out["server_tools_usd"] = (
|
|
web_searches / 1_000.0 * ANTHROPIC_WEB_SEARCH_USD_PER_1K
|
|
+ code_exec_hours * ANTHROPIC_CODE_EXEC_USD_PER_HOUR
|
|
)
|
|
else:
|
|
# OpenAI: cache writes pay base input, only reads get 0.1x.
|
|
# Subtract cached from already-counted input_usd to avoid
|
|
# double-billing (OpenAI folds cache into input_tokens).
|
|
if cache_read > 0:
|
|
non_cached_input = max(0, input_tokens - cache_read)
|
|
out["input_usd"] = (non_cached_input / 1_000_000.0) * base
|
|
out["cache_read_usd"] = (cache_read / 1_000_000.0) * base * OPENAI_CACHE_READ_MULT
|
|
# OpenAI server-tool surcharges arrive under `openai_tool_use`
|
|
# (normalised by the SSE finaliser from output items).
|
|
srv = usage.get("openai_tool_use") or {}
|
|
if isinstance(srv, dict):
|
|
web_searches = int(srv.get("web_search_requests") or 0)
|
|
container_hours = float(srv.get("container_hours") or 0.0)
|
|
out["server_tools_usd"] = (
|
|
web_searches / 1_000.0 * OPENAI_WEB_SEARCH_USD_PER_1K
|
|
+ container_hours * OPENAI_CONTAINER_USD_PER_HOUR
|
|
)
|
|
|
|
out["total_usd"] = round(
|
|
out["input_usd"]
|
|
+ out["output_usd"]
|
|
+ out["cache_write_usd"]
|
|
+ out["cache_read_usd"]
|
|
+ out["server_tools_usd"],
|
|
6,
|
|
)
|
|
return out
|
|
|
|
|
|
def pricing_snapshot() -> dict[str, Any]:
|
|
"""Whole pricing table for the /api/providers/pricing endpoint."""
|
|
return {
|
|
"anthropic": {
|
|
"models": dict(ANTHROPIC_PRICING),
|
|
"cache_5m_write_mult": ANTHROPIC_CACHE_5M_WRITE_MULT,
|
|
"cache_1h_write_mult": ANTHROPIC_CACHE_1H_WRITE_MULT,
|
|
"cache_read_mult": ANTHROPIC_CACHE_READ_MULT,
|
|
"fast_mode_mult": ANTHROPIC_FAST_MODE_MULT,
|
|
"web_search_usd_per_1k": ANTHROPIC_WEB_SEARCH_USD_PER_1K,
|
|
"code_execution_usd_per_hour": ANTHROPIC_CODE_EXEC_USD_PER_HOUR,
|
|
},
|
|
"openai": {
|
|
"models": dict(OPENAI_PRICING),
|
|
"cache_read_mult": OPENAI_CACHE_READ_MULT,
|
|
"web_search_usd_per_1k": OPENAI_WEB_SEARCH_USD_PER_1K,
|
|
"container_usd_per_hour": OPENAI_CONTAINER_USD_PER_HOUR,
|
|
},
|
|
}
|